Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations1161
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory394.2 KiB
Average record size in memory347.7 B

Variable types

DateTime1
Categorical4
Numeric10

Alerts

department is highly overall correlated with no_of_workers and 2 other fieldsHigh correlation
idle_men is highly overall correlated with idle_timeHigh correlation
idle_time is highly overall correlated with idle_menHigh correlation
incentive is highly overall correlated with no_of_workers and 3 other fieldsHigh correlation
no_of_workers is highly overall correlated with department and 4 other fieldsHigh correlation
over_time is highly overall correlated with department and 4 other fieldsHigh correlation
smv is highly overall correlated with department and 4 other fieldsHigh correlation
wip is highly overall correlated with incentive and 3 other fieldsHigh correlation
no_of_style_change is highly imbalanced (59.3%)Imbalance
idle_time is highly skewed (γ1 = 20.23274311)Skewed
wip has 495 (42.6%) zerosZeros
over_time has 27 (2.3%) zerosZeros
incentive has 593 (51.1%) zerosZeros
idle_time has 1143 (98.4%) zerosZeros
idle_men has 1143 (98.4%) zerosZeros

Reproduction

Analysis started2024-10-16 15:42:22.414596
Analysis finished2024-10-16 15:42:33.719402
Duration11.3 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

date
Date

Distinct59
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
Minimum2015-01-01 00:00:00
Maximum2015-03-11 00:00:00
2024-10-16T10:42:33.804435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:33.940095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

quarter
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
Quarter1
348 
Quarter2
325 
Quarter4
243 
Quarter3
204 
Quarter5
41 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters9288
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuarter1
2nd rowQuarter1
3rd rowQuarter1
4th rowQuarter1
5th rowQuarter1

Common Values

ValueCountFrequency (%)
Quarter1 348
30.0%
Quarter2 325
28.0%
Quarter4 243
20.9%
Quarter3 204
17.6%
Quarter5 41
 
3.5%

Length

2024-10-16T10:42:34.060812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T10:42:34.155316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
quarter1 348
30.0%
quarter2 325
28.0%
quarter4 243
20.9%
quarter3 204
17.6%
quarter5 41
 
3.5%

Most occurring characters

ValueCountFrequency (%)
r 2322
25.0%
Q 1161
12.5%
u 1161
12.5%
a 1161
12.5%
t 1161
12.5%
e 1161
12.5%
1 348
 
3.7%
2 325
 
3.5%
4 243
 
2.6%
3 204
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2322
25.0%
Q 1161
12.5%
u 1161
12.5%
a 1161
12.5%
t 1161
12.5%
e 1161
12.5%
1 348
 
3.7%
2 325
 
3.5%
4 243
 
2.6%
3 204
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2322
25.0%
Q 1161
12.5%
u 1161
12.5%
a 1161
12.5%
t 1161
12.5%
e 1161
12.5%
1 348
 
3.7%
2 325
 
3.5%
4 243
 
2.6%
3 204
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2322
25.0%
Q 1161
12.5%
u 1161
12.5%
a 1161
12.5%
t 1161
12.5%
e 1161
12.5%
1 348
 
3.7%
2 325
 
3.5%
4 243
 
2.6%
3 204
 
2.2%

department
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
sweing
666 
finishing
495 

Length

Max length9
Median length6
Mean length7.2790698
Min length6

Characters and Unicode

Total characters8451
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsweing
2nd rowfinishing
3rd rowsweing
4th rowsweing
5th rowsweing

Common Values

ValueCountFrequency (%)
sweing 666
57.4%
finishing 495
42.6%

Length

2024-10-16T10:42:34.271830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T10:42:34.362401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sweing 666
57.4%
finishing 495
42.6%

Most occurring characters

ValueCountFrequency (%)
i 2151
25.5%
n 1656
19.6%
s 1161
13.7%
g 1161
13.7%
w 666
 
7.9%
e 666
 
7.9%
f 495
 
5.9%
h 495
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2151
25.5%
n 1656
19.6%
s 1161
13.7%
g 1161
13.7%
w 666
 
7.9%
e 666
 
7.9%
f 495
 
5.9%
h 495
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2151
25.5%
n 1656
19.6%
s 1161
13.7%
g 1161
13.7%
w 666
 
7.9%
e 666
 
7.9%
f 495
 
5.9%
h 495
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2151
25.5%
n 1656
19.6%
s 1161
13.7%
g 1161
13.7%
w 666
 
7.9%
e 666
 
7.9%
f 495
 
5.9%
h 495
 
5.9%

day
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size73.1 KiB
Wednesday
201 
Sunday
196 
Monday
195 
Tuesday
194 
Thursday
193 

Length

Max length9
Median length8
Mean length7.332472
Min length6

Characters and Unicode

Total characters8513
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Wednesday 201
17.3%
Sunday 196
16.9%
Monday 195
16.8%
Tuesday 194
16.7%
Thursday 193
16.6%
Saturday 182
15.7%

Length

2024-10-16T10:42:34.460663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T10:42:34.562291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wednesday 201
17.3%
sunday 196
16.9%
monday 195
16.8%
tuesday 194
16.7%
thursday 193
16.6%
saturday 182
15.7%

Most occurring characters

ValueCountFrequency (%)
d 1362
16.0%
a 1343
15.8%
y 1161
13.6%
u 765
9.0%
e 596
7.0%
n 592
7.0%
s 588
6.9%
T 387
 
4.5%
S 378
 
4.4%
r 375
 
4.4%
Other values (5) 966
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1362
16.0%
a 1343
15.8%
y 1161
13.6%
u 765
9.0%
e 596
7.0%
n 592
7.0%
s 588
6.9%
T 387
 
4.5%
S 378
 
4.4%
r 375
 
4.4%
Other values (5) 966
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1362
16.0%
a 1343
15.8%
y 1161
13.6%
u 765
9.0%
e 596
7.0%
n 592
7.0%
s 588
6.9%
T 387
 
4.5%
S 378
 
4.4%
r 375
 
4.4%
Other values (5) 966
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1362
16.0%
a 1343
15.8%
y 1161
13.6%
u 765
9.0%
e 596
7.0%
n 592
7.0%
s 588
6.9%
T 387
 
4.5%
S 378
 
4.4%
r 375
 
4.4%
Other values (5) 966
11.3%

team
Real number (ℝ)

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5495263
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:34.669657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4252362
Coefficient of variation (CV)0.52297465
Kurtosis-1.1945385
Mean6.5495263
Median Absolute Deviation (MAD)3
Skewness-0.030188704
Sum7604
Variance11.732243
MonotonicityNot monotonic
2024-10-16T10:42:34.767328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 108
9.3%
9 104
9.0%
4 103
8.9%
10 100
8.6%
12 98
8.4%
7 96
8.3%
1 94
8.1%
6 94
8.1%
2 94
8.1%
5 92
7.9%
Other values (2) 178
15.3%
ValueCountFrequency (%)
1 94
8.1%
2 94
8.1%
3 90
7.8%
4 103
8.9%
5 92
7.9%
6 94
8.1%
7 96
8.3%
8 108
9.3%
9 104
9.0%
10 100
8.6%
ValueCountFrequency (%)
12 98
8.4%
11 88
7.6%
10 100
8.6%
9 104
9.0%
8 108
9.3%
7 96
8.3%
6 94
8.1%
5 92
7.9%
4 103
8.9%
3 90
7.8%

targeted_productivity
Real number (ℝ)

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72796727
Minimum0.07
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:34.860277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.5
Q10.7
median0.75
Q30.8
95-th percentile0.8
Maximum0.8
Range0.73
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.098715032
Coefficient of variation (CV)0.13560367
Kurtosis5.4375308
Mean0.72796727
Median Absolute Deviation (MAD)0.05
Skewness-2.1137748
Sum845.17
Variance0.0097446575
MonotonicityNot monotonic
2024-10-16T10:42:34.982438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.8 511
44.0%
0.7 239
20.6%
0.75 213
18.3%
0.65 62
 
5.3%
0.6 57
 
4.9%
0.5 49
 
4.2%
0.35 27
 
2.3%
0.4 2
 
0.2%
0.07 1
 
0.1%
ValueCountFrequency (%)
0.07 1
 
0.1%
0.35 27
 
2.3%
0.4 2
 
0.2%
0.5 49
 
4.2%
0.6 57
 
4.9%
0.65 62
 
5.3%
0.7 239
20.6%
0.75 213
18.3%
0.8 511
44.0%
ValueCountFrequency (%)
0.8 511
44.0%
0.75 213
18.3%
0.7 239
20.6%
0.65 62
 
5.3%
0.6 57
 
4.9%
0.5 49
 
4.2%
0.4 2
 
0.2%
0.35 27
 
2.3%
0.07 1
 
0.1%

smv
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.005392
Minimum2.9
Maximum54.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:35.138921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile2.9
Q13.94
median15.26
Q324.26
95-th percentile30.1
Maximum54.56
Range51.66
Interquartile range (IQR)20.32

Descriptive statistics

Standard deviation11.004686
Coefficient of variation (CV)0.7333821
Kurtosis-0.78266506
Mean15.005392
Median Absolute Deviation (MAD)11.11
Skewness0.42984749
Sum17421.26
Variance121.10311
MonotonicityNot monotonic
2024-10-16T10:42:35.282889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.94 186
16.0%
2.9 108
 
9.3%
22.52 80
 
6.9%
30.1 79
 
6.8%
4.15 72
 
6.2%
18.79 50
 
4.3%
4.6 46
 
4.0%
15.26 44
 
3.8%
25.9 32
 
2.8%
11.61 31
 
2.7%
Other values (60) 433
37.3%
ValueCountFrequency (%)
2.9 108
9.3%
3.9 20
 
1.7%
3.94 186
16.0%
4.08 20
 
1.7%
4.15 72
 
6.2%
4.3 17
 
1.5%
4.6 46
 
4.0%
5.13 26
 
2.2%
10.05 6
 
0.5%
11.41 30
 
2.6%
ValueCountFrequency (%)
54.56 1
0.1%
51.02 1
0.1%
50.89 1
0.1%
50.48 2
0.2%
49.1 1
0.1%
48.84 2
0.2%
48.68 1
0.1%
48.18 1
0.1%
45.67 1
0.1%
42.97 2
0.2%

wip
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct532
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean665.41171
Minimum0
Maximum23122
Zeros495
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:35.427242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median562
Q31070
95-th percentile1495
Maximum23122
Range23122
Interquartile range (IQR)1070

Descriptive statistics

Standard deviation1457.2671
Coefficient of variation (CV)2.1900232
Kurtosis158.13561
Mean665.41171
Median Absolute Deviation (MAD)562
Skewness11.420898
Sum772543
Variance2123627.4
MonotonicityNot monotonic
2024-10-16T10:42:35.556789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 495
42.6%
1039 5
 
0.4%
1282 4
 
0.3%
1108 3
 
0.3%
913 3
 
0.3%
1086 3
 
0.3%
1448 3
 
0.3%
1140 3
 
0.3%
1144 3
 
0.3%
1079 3
 
0.3%
Other values (522) 636
54.8%
ValueCountFrequency (%)
0 495
42.6%
7 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
14 1
 
0.1%
15 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
ValueCountFrequency (%)
23122 1
0.1%
21540 1
0.1%
21385 1
0.1%
21266 1
0.1%
12261 1
0.1%
9792 1
0.1%
8992 1
0.1%
2984 1
0.1%
2698 1
0.1%
2120 1
0.1%

over_time
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct143
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4572.2911
Minimum0
Maximum25920
Zeros27
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:35.683192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile960
Q11440
median3960
Q36960
95-th percentile10440
Maximum25920
Range25920
Interquartile range (IQR)5520

Descriptive statistics

Standard deviation3363.5904
Coefficient of variation (CV)0.73564659
Kurtosis0.44311872
Mean4572.2911
Median Absolute Deviation (MAD)2760
Skewness0.69090782
Sum5308430
Variance11313740
MonotonicityNot monotonic
2024-10-16T10:42:35.810425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960 126
 
10.9%
1440 110
 
9.5%
6960 60
 
5.2%
1200 39
 
3.4%
6840 37
 
3.2%
10170 36
 
3.1%
1800 35
 
3.0%
3360 30
 
2.6%
4080 30
 
2.6%
7080 28
 
2.4%
Other values (133) 630
54.3%
ValueCountFrequency (%)
0 27
 
2.3%
120 1
 
0.1%
240 6
 
0.5%
360 2
 
0.2%
480 1
 
0.1%
600 4
 
0.3%
720 4
 
0.3%
840 2
 
0.2%
900 2
 
0.2%
960 126
10.9%
ValueCountFrequency (%)
25920 1
 
0.1%
15120 1
 
0.1%
15000 2
 
0.2%
14640 1
 
0.1%
13800 1
 
0.1%
12600 1
 
0.1%
12180 1
 
0.1%
12000 1
 
0.1%
10770 1
 
0.1%
10620 22
1.9%

incentive
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.981051
Minimum0
Maximum3600
Zeros593
Zeros (%)51.1%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:35.926992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile75
Maximum3600
Range3600
Interquartile range (IQR)50

Descriptive statistics

Standard deviation162.23455
Coefficient of variation (CV)4.3869642
Kurtosis293.46956
Mean36.981051
Median Absolute Deviation (MAD)0
Skewness15.692423
Sum42935
Variance26320.048
MonotonicityNot monotonic
2024-10-16T10:42:36.051118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 593
51.1%
50 113
 
9.7%
63 61
 
5.3%
45 54
 
4.7%
30 52
 
4.5%
23 38
 
3.3%
38 29
 
2.5%
60 28
 
2.4%
40 27
 
2.3%
75 24
 
2.1%
Other values (36) 142
 
12.2%
ValueCountFrequency (%)
0 593
51.1%
21 1
 
0.1%
23 38
 
3.3%
24 2
 
0.2%
25 1
 
0.1%
26 9
 
0.8%
27 2
 
0.2%
29 1
 
0.1%
30 52
 
4.5%
32 1
 
0.1%
ValueCountFrequency (%)
3600 1
 
0.1%
2880 1
 
0.1%
1440 1
 
0.1%
1200 1
 
0.1%
1080 1
 
0.1%
960 5
0.4%
113 2
 
0.2%
100 6
0.5%
98 1
 
0.1%
94 4
0.3%

idle_time
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75279931
Minimum0
Maximum300
Zeros1143
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:36.155640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum300
Range300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.904809
Coefficient of variation (CV)17.142429
Kurtosis429.23721
Mean0.75279931
Median Absolute Deviation (MAD)0
Skewness20.232743
Sum874
Variance166.5341
MonotonicityNot monotonic
2024-10-16T10:42:36.255502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1143
98.4%
3.5 3
 
0.3%
2 2
 
0.2%
5 2
 
0.2%
8 2
 
0.2%
4.5 2
 
0.2%
4 2
 
0.2%
90 1
 
0.1%
150 1
 
0.1%
270 1
 
0.1%
Other values (2) 2
 
0.2%
ValueCountFrequency (%)
0 1143
98.4%
2 2
 
0.2%
3.5 3
 
0.3%
4 2
 
0.2%
4.5 2
 
0.2%
5 2
 
0.2%
6.5 1
 
0.1%
8 2
 
0.2%
90 1
 
0.1%
150 1
 
0.1%
ValueCountFrequency (%)
300 1
 
0.1%
270 1
 
0.1%
150 1
 
0.1%
90 1
 
0.1%
8 2
0.2%
6.5 1
 
0.1%
5 2
0.2%
4.5 2
0.2%
4 2
0.2%
3.5 3
0.3%

idle_men
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38070629
Minimum0
Maximum45
Zeros1143
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:36.355949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum45
Range45
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.3186682
Coefficient of variation (CV)8.7171352
Kurtosis99.735925
Mean0.38070629
Median Absolute Deviation (MAD)0
Skewness9.7015714
Sum442
Variance11.013558
MonotonicityNot monotonic
2024-10-16T10:42:36.446955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1143
98.4%
10 3
 
0.3%
15 3
 
0.3%
30 3
 
0.3%
20 3
 
0.3%
35 2
 
0.2%
45 1
 
0.1%
37 1
 
0.1%
25 1
 
0.1%
40 1
 
0.1%
ValueCountFrequency (%)
0 1143
98.4%
10 3
 
0.3%
15 3
 
0.3%
20 3
 
0.3%
25 1
 
0.1%
30 3
 
0.3%
35 2
 
0.2%
37 1
 
0.1%
40 1
 
0.1%
45 1
 
0.1%
ValueCountFrequency (%)
45 1
 
0.1%
40 1
 
0.1%
37 1
 
0.1%
35 2
 
0.2%
30 3
 
0.3%
25 1
 
0.1%
20 3
 
0.3%
15 3
 
0.3%
10 3
 
0.3%
0 1143
98.4%

no_of_style_change
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size65.9 KiB
0
1014 
1
114 
2
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1161
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1014
87.3%
1 114
 
9.8%
2 33
 
2.8%

Length

2024-10-16T10:42:36.548919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T10:42:36.637068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1014
87.3%
1 114
 
9.8%
2 33
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 1014
87.3%
1 114
 
9.8%
2 33
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1014
87.3%
1 114
 
9.8%
2 33
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1014
87.3%
1 114
 
9.8%
2 33
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1014
87.3%
1 114
 
9.8%
2 33
 
2.8%

no_of_workers
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.32472
Minimum2
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:36.740228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median34
Q357
95-th percentile59
Maximum89
Range87
Interquartile range (IQR)48

Descriptive statistics

Standard deviation22.184086
Coefficient of variation (CV)0.64630058
Kurtosis-1.7882474
Mean34.32472
Median Absolute Deviation (MAD)24
Skewness-0.089649493
Sum39851
Variance492.13369
MonotonicityNot monotonic
2024-10-16T10:42:36.861827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 258
22.2%
58 112
 
9.6%
57 96
 
8.3%
59 75
 
6.5%
10 59
 
5.1%
56.5 53
 
4.6%
56 49
 
4.2%
34 43
 
3.7%
9 41
 
3.5%
12 37
 
3.2%
Other values (51) 338
29.1%
ValueCountFrequency (%)
2 6
 
0.5%
4 1
 
0.1%
5 3
 
0.3%
6 1
 
0.1%
7 3
 
0.3%
8 258
22.2%
9 41
 
3.5%
10 59
 
5.1%
11 1
 
0.1%
12 37
 
3.2%
ValueCountFrequency (%)
89 1
 
0.1%
60 7
 
0.6%
59.5 5
 
0.4%
59 75
6.5%
58.5 19
 
1.6%
58 112
9.6%
57.5 18
 
1.6%
57 96
8.3%
56.5 53
4.6%
56 49
4.2%

actual_productivity
Real number (ℝ)

Distinct857
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72624401
Minimum0.23370548
Maximum1.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-10-16T10:42:36.982905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.23370548
5-th percentile0.35444444
Q10.65006644
median0.75516667
Q30.84545833
95-th percentile0.95515151
Maximum1.02
Range0.78629452
Interquartile range (IQR)0.19539189

Descriptive statistics

Standard deviation0.16955038
Coefficient of variation (CV)0.233462
Kurtosis0.37360177
Mean0.72624401
Median Absolute Deviation (MAD)0.095057109
Skewness-0.90595673
Sum843.16929
Variance0.028747331
MonotonicityNot monotonic
2024-10-16T10:42:37.115135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.800401961 24
 
2.1%
0.971866667 12
 
1.0%
0.850136766 12
 
1.0%
0.75065101 11
 
0.9%
0.850502311 11
 
0.9%
0.750395513 8
 
0.7%
0.800128721 8
 
0.7%
0.8 7
 
0.6%
0.858143939 7
 
0.6%
0.800117103 7
 
0.6%
Other values (847) 1054
90.8%
ValueCountFrequency (%)
0.233705476 1
0.1%
0.235795455 1
0.1%
0.238041667 1
0.1%
0.24625 1
0.1%
0.247316017 1
0.1%
0.249416667 1
0.1%
0.251399254 1
0.1%
0.2565 1
0.1%
0.258 1
0.1%
0.259375 1
0.1%
ValueCountFrequency (%)
1.02 1
 
0.1%
0.999995238 3
0.3%
0.999924242 1
 
0.1%
0.999533333 1
 
0.1%
0.997792208 1
 
0.1%
0.99485 1
 
0.1%
0.994375 1
 
0.1%
0.994270833 1
 
0.1%
0.9918 1
 
0.1%
0.991388889 1
 
0.1%

Interactions

2024-10-16T10:42:32.161900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:22.931017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.846637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:25.423256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.341339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.205262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.223624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.406041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.418986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.298491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.263279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.021886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.933653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:25.514535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.425357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.296015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.312880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.494498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.506756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.379419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.363223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.120628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.029742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:25.645179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.518228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.408680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.421006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.589126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.599458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.472307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.450406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.204209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.116131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:25.739402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.598339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.539847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.512772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.676544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.679760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.556516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.568091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.287629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.205700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:25.821364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.679966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.666709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.609524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.763134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.762335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.638463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.683869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.371377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.299968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:25.908311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.763438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.762439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.701563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.857721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.845918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.724823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.791238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.463558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.396515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:25.997880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.854995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.866377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.815092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.959272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.939065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.816725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.898759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.553848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.493595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.082007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.941690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.957568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.117481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.095688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.024576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.905116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.984451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.659151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.579318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.162663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.022255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.045818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.207083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.212377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.105778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.986272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:33.074065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:23.748935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:24.668332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:26.242150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:27.106939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:28.126343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:29.298916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:30.313532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:31.204975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T10:42:32.065339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-16T10:42:37.212737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
actual_productivitydaydepartmentidle_menidle_timeincentiveno_of_style_changeno_of_workersover_timequartersmvtargeted_productivityteamwip
actual_productivity1.0000.0220.391-0.148-0.1480.1840.199-0.061-0.0790.128-0.1380.439-0.110-0.100
day0.0221.0000.0000.0000.0080.0800.0000.0000.0280.1370.0000.0440.0000.059
department0.3910.0001.0000.0690.0000.0910.3260.9930.7740.0000.9970.0570.0530.039
idle_men-0.1480.0000.0691.0001.000-0.0520.1790.138-0.0180.0320.139-0.0570.0180.010
idle_time-0.1480.0080.0001.0001.000-0.0520.0000.138-0.0180.0000.139-0.0580.0170.009
incentive0.1840.0800.091-0.052-0.0521.0000.0000.6500.5490.0460.5990.1800.0310.728
no_of_style_change0.1990.0000.3260.1790.0000.0001.0000.3560.2250.1890.4240.1840.1530.000
no_of_workers-0.0610.0000.9930.1380.1380.6500.3561.0000.7520.0330.892-0.070-0.1030.753
over_time-0.0790.0280.774-0.018-0.0180.5490.2250.7521.0000.1230.704-0.079-0.1070.674
quarter0.1280.1370.0000.0320.0000.0460.1890.0330.1231.0000.1300.1050.0000.016
smv-0.1380.0000.9970.1390.1390.5990.4240.8920.7040.1301.000-0.104-0.1030.738
targeted_productivity0.4390.0440.057-0.057-0.0580.1800.184-0.070-0.0790.105-0.1041.0000.072-0.098
team-0.1100.0000.0530.0180.0170.0310.153-0.103-0.1070.000-0.1030.0721.0000.057
wip-0.1000.0590.0390.0100.0090.7280.0000.7530.6740.0160.738-0.0980.0571.000

Missing values

2024-10-16T10:42:33.435252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T10:42:33.634875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
01/01/2015Quarter1sweingThursday80.8026.161108.07080980.00059.00.940725
11/01/2015Quarter1finishingThursday10.753.940.096000.0008.00.886500
21/01/2015Quarter1sweingThursday110.8011.41968.03660500.00030.50.800570
31/01/2015Quarter1sweingThursday120.8011.41968.03660500.00030.50.800570
41/01/2015Quarter1sweingThursday60.8025.901170.01920500.00056.00.800382
51/01/2015Quarter1sweingThursday70.8025.90984.06720380.00056.00.800125
61/01/2015Quarter1finishingThursday20.753.940.096000.0008.00.755167
71/01/2015Quarter1sweingThursday30.7528.08795.06900450.00057.50.753683
81/01/2015Quarter1sweingThursday20.7519.87733.06000340.00055.00.753098
91/01/2015Quarter1sweingThursday10.7528.08681.06900450.00057.50.750428
datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
11513/11/2015Quarter2sweingWednesday40.7526.821054.07080450.00059.00.750051
11523/11/2015Quarter2sweingWednesday50.7026.82992.06960300.00158.00.700557
11533/11/2015Quarter2sweingWednesday80.7030.48914.06840300.00157.00.700505
11543/11/2015Quarter2sweingWednesday60.7023.411128.04560400.00138.00.700246
11553/11/2015Quarter2sweingWednesday70.6530.48935.06840260.00157.00.650596
11563/11/2015Quarter2finishingWednesday100.752.900.096000.0008.00.628333
11573/11/2015Quarter2finishingWednesday80.703.900.096000.0008.00.625625
11583/11/2015Quarter2finishingWednesday70.653.900.096000.0008.00.625625
11593/11/2015Quarter2finishingWednesday90.752.900.0180000.00015.00.505889
11603/11/2015Quarter2finishingWednesday60.702.900.072000.0006.00.394722